Detecting fault zone characteristics and paleovalley incision using electrical resistivity: Loma Blanca Fault, New Mexico

Abstract

This study combines electrical resistivity tomography (ERT), geological information from boreholes and outcrops, and hydrogeologic data to investigate field-scale fault-zone cementation of the Loma Blanca Fault in the Rio Grande Rift. We collected electrical resistivity data from 16 transects and geological samples from 29 boreholes (completed as groundwater wells to 30 m depth) across and around the fault. 2D ERT profiles, whose interpretations are constrained by geological data, indicate: (1) a high resistivity zone in cemented portions of the fault below the water table, and (2) in the unsaturated zone, a low resistivity feature along the cemented portions of the fault. The high resistivity zone below the water table is consistent with a 10% reduction in porosity due to the fault zone cementation. With the same porosity in the unsaturated zone, the low resistivity feature in the cemented fault zone is consistent with saturation >0.7, in contrast to saturation 0.2-0.7 for sediment outside of the cemented fault zone. In addition, subsurface samples and ERT profiles delineate a buttress unconformity (i.e., steeply dipping erosional contact) corresponding to a paleo-valley margin. This unconformity truncates the cemented fault zone and separates Pliocene axial-fluvial sand (deposited by an ancestral Rio Grande) from late Quaternary sand and gravel (deposited by the Rio Salado, a Rio Grande tributary). The cemented fault zone in the southern portion of the study area is a hydrogeologic barrier; north of the buttress unconformity, where the cemented fault zone has been removed by erosion, the fault is not a hydrogeologic barrier. The integration of geological, geophysical, and hydrogeological observations was key to developing our understanding of this complex system, and allowed us to demonstrate the utility of ERT in detecting subsurface fault-zone cementation.

Robot Fault Detection Based on Big Data

“…Te fault detection and diagnosis of robot joint sensors can be realized, but the sensor information redundancy must be required [5]. In order to solve the fault diagnosis problem of the manipulator, H Barnes designed a novel fault diagnosis algorithm using the sliding mode observer and performed experiments on the COMAU manipulator, but this method could not realize the detection of concurrent faults [6]. Lee, in order to solve the problem that the error convergence of the classical sliding mode observer is slow, which afects the real-time performance of the fault diagnosis of the underwater robot, based on the fast convergence characteristics of the terminal sliding mode, the observer is designed to achieve rapid convergence of all estimation errors, and the equivalent error injection is adopted, the fault value is reconstructed by the method, and the fault diagnosis of the propulsion system of the underwater robot is realized [7].…”

Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys

“…Therefore, faults are considered a crucial aspect in investigations of site characteristics for largescale construction, such as geological surveys, tunnels, and bridges, as well as in ground stability assessment, including ground subsidence, tunnel collapse, and slope stability investigations [1][2][3]. Geophysical exploration techniques, including seismic, electrical, and electromagnetic methods, are actively being used in various fields to detect faults [4][5][6][7][8][9]. In particular, fault zones, weathered zones, and aquifers containing clay minerals exhibit low electrical resistivity, making electrical resistivity tomography (ERT) surveys an effective tool in fault zone investigations [10].…”